3 AI Advances From Around the World in 2017

The past few years have seen many improvements in automation, including cheaper sensors, more powerful processors, and most importantly, a better understanding of how artificial intelligence and robotics can help businesses. 2017 brought even more AI advances.

With the recent development of machine learning techniques, it’s reasonable to expect that scientists, software engineers, and enterprises will try to apply them to everything from self-driving cars to social robots. We have already witnessed Google using reinforcement learning to make its data centers more energy-efficient.

Here are some of the most important AI and robotics advancements from the past year.

The Chinese government and investors are looking to pour a lot of money into AI projects as the country looks to build an industry of its own. They planned to invest around $15 billion dollars by the end of 2017, and this year will likely bring more.

Improving speech recognition helps robots become more user-friendly.

2. Learning languages helps robots

If you ask any AI researcher what are the next things, chances are that one of the things he or she will mention is language. The use of speech and image recognition to analyze inflections and facial expressions will help machines interact more naturally with their human users.

This is an important goal and will make AI and robots easier to use and increase their functionality. Chatbots are already paving the way in customer service, and many industry observers expect voice interfaces to make industrial and consumer robots easier to use.

However, this is still a big challenge for AI because languages are complex, and “smart” machines need a certain level of intelligence to respond and carry out voice commands. We may not yet be able to engage our smartphones in deep conversations, but we can expect more breakthroughs in the coming year.

3. Machine learning develops

Last year, the Neural Information Processing Systems conference that took place in Barcelona was very exciting. The main topic was new techniques in machine learning. The hottest one is called a generative adversarial network, or GAN.

AI advances in 2017 included new techniques in machine learning.

These systems consist out of one network that generates brand-new data after it has gone through training and learned new things and another network designed to distinguish fake data from real data. When combined, these two networks have the power to create realistic synthetic data that can be very useful.

With this approach, it’s possible to generate video games, correct pixelated videos, or make changes to computer-generated designs. Such AI advances could help computers learn from data that is unlabeled. This is an essential step to making robots and AI more intelligent in the future.

Although AI advances have been steady, there weren’t any surprising breakthroughs this past year. That doesn’t mean that nothing exciting is happening, but robotics and AI developers and prospective users should be wary of news media hype.

Business and consumer expectations are so high that people are impatient or disappointed when the learn the technical particulars of the latest AI advances. However, there is a lot going on in this field, with too many potential applications to list, so the future is bright.

About the Author: Catherine Park is a professional content writer and a blogger full of energy and positivism. She is an expert in writing exclusive content on business and technologies that are helpful for enterprises of all sizes and business startups. Follow her on Twitter and Facebook for upcoming articles!